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Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood.
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‘Improving ratings’: audit in the British University system
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A comparative study on feature selection in text categorization
Yiming Yang and Jan O. Pedersen · 1997
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Measuring individual differences in implicit cognition: the implicit association test
Anthony G Greenwald, Debbie E McGhee, and Jordan LK Schwartz · 1998
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Text categorization with support vector machines: Learning with many relevant features
Thorsten Joachims · 1998
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The Text REtrieval Conferences (TRECs)
Ellen M. Voorhees and Donna Harman · 1998
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Advances in Automatic Text Summarization
Inderjeet Mani · 1999
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Mitchell Marcus, Beatrice Santorini, Mary Ann Marcinkiewicz, and Ann Taylor · 1999
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Automatic summarizing: factors and directions
Karen Spärck Jones · 1999
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An evaluation of statistical approaches to text categorization
Yiming Yang · 1999
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Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition
Daniel Jurafsky and James H Martin · 2000
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A probabilistic Earley parser as a psycholinguistic model
John Hale · 2001
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Cumulated gain-based evaluation of ir techniques
Kalervo Järvelin and Jaana Kekäläinen · 2002
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Thumbs up? sentiment classification using machine learning techniques
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan · 2002
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Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews
Peter Turney · 2002
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The state and fate of linguistic diversity and inclusion in the nlp world
Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhury · 2004
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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ACL lifetime achievement award: Some points in a time
Karen Spärck Jones · 2005
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Annotating expressions of opinions and emotions in language
Janyce Wiebe, Theresa Wilson, and Claire Cardie · 2005
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Expectation-based syntactic comprehension
Roger Levy · 2007
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
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Measurement
Simon Jackman · 2008
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Introduction to information retrieval
Christopher D Manning, Prabhakar Raghavan, and Hinrich Schütze · 2008
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Opinion mining and sentiment analysis
Bo Pang and Lillian Lee · 2008
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Linguistically naïve != language independent: Why NLP needs linguistic typology
Emily M. Bender · 2009
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Braj B Kachru, Yamuna Kachru, and Cecil L Nelson · 2009
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The probabilistic relevance framework: BM25 and beyond
Stephen Robertson and Hugo Zaragoza · 2009
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On the foundations of noise-free selective classification
Ran El-Yaniv and Yair Wiener · 2010
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Word representations: a simple and general method for semi-supervised learning
Joseph Turian, Lev Ratinov, and Yoshua Bengio · 2010
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On achieving and evaluating language-independence in nlp
Emily M. Bender · 2011
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Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa · 2011
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Glottolog/langdoc: Defining dialects, languages, and language families as collections of resources
Sebastian Nordhoff and Harald Hammarström · 2011
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Generating text with recurrent neural networks
Ilya Sutskever, James Martens, and Geoffrey E Hinton · 2011
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A survey of text classification algorithms
Charu C. Aggarwal and ChengXiang Zhai · 2012
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100 things you always wanted to know about linguistics but were afraid to ask*
Emily M. Bender · 2012
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel · 2012
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Learning attitudes and attributes from multi-aspect reviews
Julian McAuley, Jure Leskovec, and Dan Jurafsky · 2012
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A survey of text summarization techniques
Ani Nenkova and Kathleen McKeown · 2012
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Rationality
Gilbert Harman · 2013
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Y Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Discrimination in online ad delivery
Latanya Sweeney · 2013
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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A large annotated corpus for learning natural language inference
Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D. Manning · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht · 2015
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Towards ai-complete question answering: A set of prerequisite toy tasks
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M Rush, Bart Van Merriënboer, Armand Joulin, and Tomas Mikolov · 2015
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Detecting data errors: Where are we and what needs to be done?
Ziawasch Abedjan, Xu Chu, Dong Deng, Raul Castro Fernandez, Ihab F Ilyas, Mourad Ouzzani, Paolo Papotti, Michael Stonebraker, and Nan Tang · 2016
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Big data’s disparate impact
Solon Barocas and Andrew D. Selbst · 2016
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Demographic dialectal variation in social media: A case study of African-American English
Su Lin Blodgett, Lisa Green, and Brendan O’Connor · 2016
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Man is to computer programmer as woman is to homemaker? Debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
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Magellan: toward building entity matching management systems over data science stacks
Pradap Konda, Sanjib Das, AnHai Doan, Adel Ardalan, Jeffrey R Ballard, Han Li, Fatemah Panahi, Haojun Zhang, Jeff Naughton, Shishir Prasad, et al · 2016
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Ask me anything: Dynamic memory networks for natural language processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher · 2016
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Assessing the ability of LSTMs to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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SemEval-2016 task 4: Sentiment analysis in Twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov · 2016
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MS MARCO: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Finding alternative translations in a large corpus of movie subtitle
Jörg Tiedemann · 2016
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Dialog-based language learning
Jason E Weston · 2016
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Su Lin Blodgett and Brendan OConnor · 2017
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan · 2017
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Dear mona, what’s the most common name in america, 2017
Mona Chalabi and Andrew Flowers · 2017
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Dawnbench: An end-to-end deep learning benchmark and competition
Cody Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi, Peter Bailis, Kunle Olukotun, Chris Ré, and Matei Zaharia · 2017
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The WebNLG challenge: Generating text from RDF data
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini · 2017
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
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Data integration: After the teenage years
Behzad Golshan, Alon Halevy, George Mihaila, and Wang-Chiew Tan · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems
Ayanna M. Howard, Cha Zhang, and Eric Horvitz · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
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The NarrativeQA reading comprehension challenge
Tomš Kočisky, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gabor Melis, and Edward Grefenstette · 2017
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Counterfactual fairness
Matt J Kusner, Joshua R Loftus, Chris Russell, and Ricardo Silva · 2017
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Obituary: Fred jelinek
Mark Liberman · 2017
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The Enigma of Reason
Hugo Mercier and Dan Sperber · 2017
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The E2E dataset: New challenges for end-to-end generation
Jekaterina Novikova, Ondřej Dušek, and Verena Rieser · 2017
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On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q. Weinberger · 2017
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A survey on hate speech detection using natural language processing
Anna Schmidt and Michael Wiegand · 2017
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Artificial intelligence’s fair use crisis
Benjamin LW Sobel · 2017
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Understanding abuse: A typology of abusive language detection subtasks
Zeerak Talat, Thomas Davidson, Dana Warmsley, and Ingmar Weber · 2017
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NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Anserini: Enabling the use of lucene for information retrieval research
Peilin Yang, Hui Fang, and Jimmy Lin · 2017
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Fa*ir: A fair top-k ranking algorithm
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates · 2017
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Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk · 2018
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3. Epistemic Crisis
Yochai Benkler, Robert Faris, and Hal Roberts · 2018
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Human Rights Impact Assessment: Facebook in Myanmar
BSR · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Aggressive language in an online hacking forum
Andrew Caines, Sergio Pastrana, Alice Hutchings, and Paula Buttery · 2018
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Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li · 2018
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QuAC: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer · 2018
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Senteval: An evaluation toolkit for universal sentence representations
Alexis Conneau and Douwe Kiela · 2018
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Report of the independent international fact-finding mission on myanmar. united nations., 2018
Human Rights Council · 2018
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Convolutional neural networks for soft-matching n-grams in ad-hoc search
Zhuyun Dai, Chenyan Xiong, Jamie Callan, and Zhiyuan Liu · 2018
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Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Łukasz Kaiser · 2018
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New Work on Speech Acts
Daniel Fogal, Daniel W. Harris, and Matt Moss · 2018
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Word embeddings quantify 100 years of gender and ethnic stereotypes
Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou · 2018
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Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Max Grusky, Mor Naaman, and Yoav Artzi · 2018
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Context-free transductions with neural stacks
Yiding Hao, William Merrill, Dana Angluin, Robert Frank, Noah Amsel, Andrew Benz, and Simon Mendelsohn · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
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Glottolog 4.4
Harald Hammarström, Robert Forkel, Martin Haspelmath, and Sebastian Bank · 2021
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{DEBERTA}: {DECODING}-{enhanced} {bert} {with} {disentangled} {attention}
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen · 2021
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From toxicity in online comments to incivility in American news: Proceed with caution
Anushree Hede, Oshin Agarwal, Linda Lu, Diana C. Mutz, and Ani Nenkova · 2021
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The importance of modeling social factors of language: Theory and practice
Dirk Hovy and Diyi Yang · 2021
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Selective classification can magnify disparities across groups
Erik Jones, Shiori Sagawa, Pang Wei Koh, Ananya Kumar, and Percy Liang · 2021
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Aligning artificial intelligence with climate change mitigation
Lynn Kaack, Priya Donti, Emma Strubell, George Kamiya, Felix Creutzig, and David Rolnick · 2021
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Stephen Merity, Nitish Shirish Keskar, and Richard Socher · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
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21 Fairness Definitions and their Politics
Arvind Narayanan · 2018
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Algorithms of Oppression
Safiya Umoja Noble · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Improving language understanding by generative pre-training
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A moderate proposal for radically better AI-powered Web search
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Dynabench: Rethinking benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, and Adina Williams · 2021
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Reduced, reused and recycled: The life of a dataset in machine learning research
Bernard Koch, Emily Denton, Alex Hanna, and Jacob Gates Foster · 2021
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WILDS: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
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Mind the gap: Assessing temporal generalization in neural language models
Angeliki Lazaridou, Adhi Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d'Autume, Tomas Kocisky, Sebastian Ruder, Dani Yogatama, Kris Cao, Susannah Young, and Phil Blunsom · 2021
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How many data points is a prompt worth?
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Deduplicating training data makes language models better
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The power of scale for parameter-efficient prompt tuning
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Prefix-tuning: Optimizing continuous prompts for generation
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Are we learning yet? a meta review of evaluation failures across machine learning
Thomas Liao, Rohan Taori, Inioluwa Deborah Raji, and Ludwig Schmidt · 2021
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Jurassic-1: Technical details and evaluation
Opher Lieber, Or Sharir, Barak Lenz, and Yoav Shoham · 2021
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Dynaboard: An evaluation-as-a-service platform for holistic next-generation benchmarking
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Capturing semantics for imputation with pre-trained language models
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Formal language theory meets modern nlp
William Merrill · 2021
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Rethinking search: Making domain experts out of dilettantes
Donald Metzler, Yi Tay, Dara Bahri, and Marc Najork · 2021
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt · 2021
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Rewards with negative examples for reinforced topic-focused abstractive summarization
Khalil Mrini, Can Liu, and Markus Dreyer · 2021
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WebGPT: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, S. Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, V. Kosaraju, W. Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and J. Schulman · 2021
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Efficient Large-Scale Language Model Training on GPU Clusters using Megatron-LM
Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, et al · 2021
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The long fuse: Misinformation and the 2020 election, 2021
Election Integrity Partnership · 2021
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Carbon emissions and large neural network training
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean · 2021
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Data and its (dis)contents: A survey of dataset development and use in machine learning research
Amandalynne Paullada, Inioluwa Deborah Raji, Emily M. Bender, Emily Denton, and Alex Hanna · 2021
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A Practical Guide to Doing Behavioral Research on Fake News and Misinformation
Gordon Pennycook, Jabin Binnendyk, Christie Newton, and David G. Rand · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho · 2021
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DynaSent: A dynamic benchmark for sentiment analysis
Christopher Potts, Zhengxuan Wu, Atticus Geiger, and Douwe Kiela · 2021
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Scaling language models: Methods, analysis & insights from training gopher
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AI and the everything in the whole wide world benchmark
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Changing the world by changing the data
Anna Rogers · 2021
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Qa dataset explosion: A taxonomy of nlp resources for question answering and reading comprehension
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Multitask prompted training enables zero-shot task generalization
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Dgx-a100 face to face dgx-2—performance, power and thermal behavior evaluation
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Behavior: Benchmark for everyday household activities in virtual, interactive, and ecological environments
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DABS: a domain-agnostic benchmark for self-supervised learning
Alex Tamkin, Vincent Liu, Rongfei Lu, Daniel Fein, Colin Schultz, and Noah Goodman · 2021
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How well do you know your summarization datasets?
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Learning Through the Lens of Robustness
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GPT-J-6B: A 6 billion parameter autoregressive language model, 2021
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Lime: Learning inductive bias for primitives of mathematical reasoning
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Detoxifying language models risks marginalizing minority voices
Albert Xu, Eshaan Pathak, Eric Wallace, Suchin Gururangan, Maarten Sap, and Dan Klein · 2021
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QA-GNN: Reasoning with language models and knowledge graphs for question answering
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Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
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Ar-lsat: Investigating analytical reasoning of text, 2021
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Ask me anything: A simple strategy for prompting language models
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Quantifying memorization across neural language models
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HAPI: A large-scale longitudinal dataset of commercial ML API predictions
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Legalbench: Prototyping a collaborative benchmark for legal reasoning
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Towards a unified view of parameter-efficient transfer learning
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Gpt-4chan, 2022
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Training language models to follow instructions with human feedback
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Perturbation augmentation for fairer nlp
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Dog whistles, covertly coded speech, and the practices that enable them
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The fallacy of ai functionality
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Summarisation datasets should contain summaries!
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Large language models are not zero-shot communicators
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Improving passage retrieval with zero-shot question generation
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Bloom: A 176b-parameter open-access multilingual language model
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Structured access: an emerging paradigm for safe ai deployment, 2022
Toby Shevlane · 2022
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Using deepspeed and megatron to train Megatron-Turing NLG 530b, a large-scale generative language model
Shaden Smith, M. Patwary, Brandon Norick, P. LeGresley, Samyam Rajbhandari, J. Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, V. Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, J. Bernauer, Xia Song, M. Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, and Bryan Catanzaro · 2022
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Alexatm 20b: Few-shot learning using a large-scale multilingual seq2seq model
Saleh Soltan, Shankar Ananthakrishnan, Jack G. M. FitzGerald, Rahul Gupta, Wael Hamza, Haidar Khan, Charith S. Peris, Stephen Rawls, Andrew Rosenbaum, Anna Rumshisky, Chandan Prakash, Mukund Sridhar, Fabian Triefenbach, Apurv Verma, Gokhan Tur, and Premkumar Natarajan · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek B Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ameet Annasaheb Rahane, Anantharaman S. Iyer, Anders Johan Andreassen, Andrea Santilli, Andreas Stuhlmuller, Andrew M. Dai, Andrew D. La, Andrew Kyle Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakacs, Bridget R. 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Upstream Mitigation Is Not
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Challenging big-bench tasks and whether chain-of-thought can solve them
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DABS 2.0: Improved datasets and algorithms for universal self-supervision
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Galactica: A large language model for science
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Lamda: Language models for dialog applications
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Evaluate&evaluation on the hub: Better best practices for data and model measurements
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DeepStruct: Pretraining of language models for structure prediction
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Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts
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Inherent tradeoffs in learning fair representations
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Large language models are human-level prompt engineers
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Value: Understanding dialect disparity in nlu, 2022
Caleb Ziems, Jiaao Chen, Camille Harris, Jessica Anderson, and Diyi Yang · 2022
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